Optimizing soybean-pig integration zones in China: a multi-algorithm approach using random forest, LSTM, and clustering for feed grain and meat security balance

Introduction The rising concerns about food security and the increasing demands for meat-based diet in China have highlighted the imbalance between the supply and demand of its feed grains. Scholars view the imports of soybeans as an unsustainable way of ensuring feed grain security. Therefore, this study investigates the optimization of soybean and pig integration zones in China. Methods The study has adopted a multi algorithm approach based on Random Forest Model, LSTM model, KMeans clustering, and PCA to highlight the factors influencing the integration of feed grain plantation and meat security. Results The results of the PCA report that consumption, production levels, land availability, and pig quantity play an instrumental role in defining the integration of soybeans and pig farming. The results indicate that LSTM offers accurate predictions regarding the integration zones. Conclusion The study concludes that areas with high consumption of meat and large production volumes offer an opportunity to integrate soybean cultivation and pig farming in China.